TY  - JOUR
AU  - Makarious, Mary B.
AU  - Leonard, Hampton L.
AU  - Vitale, Dan
AU  - Iwaki, Hirotaka
AU  - Sargent, Lana
AU  - Dadu, Anant
AU  - Violich, Ivo
AU  - Hutchins, Elizabeth
AU  - Saffo, David
AU  - Bandres-Ciga, Sara
AU  - Kim, Jonggeol Jeff
AU  - Song, Yeajin
AU  - Maleknia, Melina
AU  - Bookman, Matt
AU  - Nojopranoto, Willy
AU  - Campbell, Roy H.
AU  - Hashemi, Sayed Hadi
AU  - Botia, Juan A.
AU  - Carter, John F.
AU  - Craig, David W.
AU  - Van Keuren-Jensen, Kendall
AU  - Morris, Huw R.
AU  - Hardy, John A.
AU  - Blauwendraat, Cornelis
AU  - Singleton, Andrew B.
AU  - Faghri, Faraz
AU  - Nalls, Mike A.
TI  - Multi-modality machine learning predicting Parkinson’s disease
JO  - npj Parkinson's Disease
VL  - 8
IS  - 1
SN  - 2373-8057
CY  - London [u.a.]
PB  - Nature Publ. Group
M1  - DZNE-2022-00445
SP  - 35
PY  - 2022
AB  - Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug–gene interactions. We performed automated ML on multimodal data from the Parkinson’s progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson’s Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72
LB  - PUB:(DE-HGF)16
C2  - pmc:PMC8975993
C6  - pmid:35365675
DO  - DOI:10.1038/s41531-022-00288-w
UR  - https://pub.dzne.de/record/163706
ER  -